Automatic Environmental Sound Recognition: Performance Versus Computational Cost
- Submitting institution
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The University of Surrey
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9016388_4
- Type
- D - Journal article
- DOI
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10.1109/TASLP.2016.2592698
- Title of journal
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Article number
- -
- First page
- 2096
- Volume
- 24
- Issue
- 11
- ISSN
- 2329-9290
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Work contributed to award of €3.8M EU H2020 642685 MacSeNet. Key outcome of industry/university collaboration with Audio Analytic (InnovateUK/EPSRC, 2014-15) to develop their pivotal Smart Microphone 2.0 technology demonstrator [https://www.realwire.com/releases/audio-analytic-expands-to-accelerate-growth-in-smart-home-market]. Demonstrator contributed to Audio Analytic (founded 2010) becoming a world leader in sound recognition, raising ~$20M funding since 2015, growing from handful of employees to ~50 people (2020), and winning multiple awards (e.g. Global Annual Achievement Award for AI 2018, AI Breakthrough Award 2020).
- Author contribution statement
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- Non-English
- No
- English abstract
- -